no code implementations • 6 Oct 2022 • Soumya Basu, Ankit Singh Rawat, Manzil Zaheer
The second class of retrieval-based approaches we explore learns a global model using kernel methods to directly map an input instance and retrieved examples to a prediction, without explicitly solving a local learning task.
no code implementations • NeurIPS 2021 • Soumya Basu, Branislav Kveton, Manzil Zaheer, Csaba Szepesvári
We propose ${\tt AdaTS}$, a Thompson sampling algorithm that adapts sequentially to bandit tasks that it interacts with.
no code implementations • 7 Jul 2021 • Nihal Sharma, Soumya Basu, Karthikeyan Shanmugam, Sanjay Shakkottai
The agent interacts with the environment over episodes, with each episode having different context distributions; this results in the `best expert' changing across episodes.
no code implementations • 22 May 2021 • Alexia Atsidakou, Orestis Papadigenopoulos, Soumya Basu, Constantine Caramanis, Sanjay Shakkottai
Recent work has considered natural variations of the multi-armed bandit problem, where the reward distribution of each arm is a special function of the time passed since its last pulling.
no code implementations • 12 Mar 2021 • Soumya Basu, Karthik Abinav Sankararaman, Abishek Sankararaman
We design decentralized algorithms for regret minimization in the two-sided matching market with one-sided bandit feedback that significantly improves upon the prior works (Liu et al. 2020a, 2020b, Sankararaman et al. 2020).
1 code implementation • 17 Feb 2021 • Ashish Katiyar, Soumya Basu, Vatsal Shah, Constantine Caramanis
Furthermore, we present a polynomial time, sample efficient algorithm that recovers the exact tree when this is possible, or up to the unidentifiability as promised by our characterization, when full recoverability is impossible.
no code implementations • 28 Nov 2020 • Vatsal Shah, Soumya Basu, Anastasios Kyrillidis, Sujay Sanghavi
In this paper, we aim to characterize the performance of adaptive methods in the over-parameterized linear regression setting.
no code implementations • 2 Nov 2020 • Advait Parulekar, Soumya Basu, Aditya Gopalan, Karthikeyan Shanmugam, Sanjay Shakkottai
We study a variant of the stochastic linear bandit problem wherein we optimize a linear objective function but rewards are accrued only orthogonal to an unknown subspace (which we interpret as a \textit{protected space}) given only zero-order stochastic oracle access to both the objective itself and protected subspace.
no code implementations • 26 Jun 2020 • Abishek Sankararaman, Soumya Basu, Karthik Abinav Sankararaman
Online learning in a two-sided matching market, with demand side agents continuously competing to be matched with supply side (arms), abstracts the complex interactions under partial information on matching platforms (e. g. UpWork, TaskRabbit).
no code implementations • 6 Mar 2020 • Soumya Basu, Orestis Papadigenopoulos, Constantine Caramanis, Sanjay Shakkottai
Assuming knowledge of the context distribution and the mean reward of each arm-context pair, we cast the problem as an online bipartite matching problem, where the right-vertices (contexts) arrive stochastically and the left-vertices (arms) are blocked for a finite number of rounds each time they are matched.
no code implementations • 19 Feb 2020 • Nihal Sharma, Soumya Basu, Karthikeyan Shanmugam, Sanjay Shakkottai
We study a variant of the multi-armed bandit problem where side information in the form of bounds on the mean of each arm is provided.
no code implementations • NeurIPS 2019 • Soumya Basu, Rajat Sen, Sujay Sanghavi, Sanjay Shakkottai
We show that with prior knowledge of the rewards and delays of all the arms, the problem of optimizing cumulative reward does not admit any pseudo-polynomial time algorithm (in the number of arms) unless randomized exponential time hypothesis is false, by mapping to the PINWHEEL scheduling problem.
no code implementations • ICML 2020 • Jessica Hoffmann, Soumya Basu, Surbhi Goel, Constantine Caramanis
When the conditions are met, i. e., when the graphs are connected with at least three edges, we give an efficient algorithm for learning the weights of both graphs with optimal sample complexity (up to log factors).
no code implementations • 11 Jan 2018 • Adem Efe Gencer, Soumya Basu, Ittay Eyal, Robbert van Renesse, Emin Gün Sirer
Blockchain-based cryptocurrencies have demonstrated how to securely implement traditionally centralized systems, such as currencies, in a decentralized fashion.
Cryptography and Security
no code implementations • 17 Mar 2017 • Aman Agarwal, Soumya Basu, Tobias Schnabel, Thorsten Joachims
In this paper, we address the question of how to estimate the performance of a new target policy when we have log data from multiple historic policies.